Massive Fracturing Evaluation to Different Well Patterns for Daqing Tight Oil Reservoirs
Bibliographic record
Abstract
Abstract Daqing oilfield has been produced for more than 50 years. Oil production from conventional oil pools is dropping significantly. But in recent years, massive fracturing technology were applied to difficult tight oil reservoir and unconventional gas development. More and more difficult reserves can be unlocked with this technology and that will revive oil production in Daqing field in near future, because the estimated recoverable tight oil & gas is more than conventional oil produced in past 50 years. To develop these tight oil reservoirs such as Zhou-6 and Shu-2, water injection and re-frackstimulation are required to provide economic production rate for running field under low oil price market environment. Ageneric workflow combining reservoir qualities and engineering parameterswas proposed in this paper. The advantage of this new workflow can consider all aspects and interactions from historic reservoir pore pressure, saturation and stress to frack pumping schedule, fluid and proppant volume which will impact re-frack fracture network development in great detail and production behavior of re-frack well in an integrated model and simulation process. At the same time, how varying well spacing and injection schema impact the fracture complexity and stimulated reservoir volumeof massive re-frack stimulations. This case study concludes that an integrated new workflow from reservoir characterization, geomechanics analysis, stimulation engineering to history matching with calibration of micro-seismic data and production performance data is possible and feasible to precisely simulate re-frack wells production behavior under varying well patterns and water injectivities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".